Generative AI is experiencing increasingly widespread adoption, accompanied by the emergence of numerous foundational models. Notably, there is a surge in “AI-first” products wherein Generative AI serves as the sole or primary feature for specific use cases. A growing number of established products are contemplating the integration of Generative AI capabilities into their existing frameworks.
Let’s look at the top 5 typical challenges that you’ll need to consider when integrating Generative AI into existing products:
- Using Generative AI Beyond Chat Requires Deep Expertise
While conversational interfaces, such as chatbots, demonstrate impressive utility in certain contexts like customer support, their applicability is limited to specific scenarios. Generative AI exhibits potential in non-conversational scenarios, offering significant value beyond traditional chat-type features. Applying generative AI to different business scenarios requires expertise to tailor prompt engineering and optimize performance. - Choosing the Right Model for Data Privacy Is Crucial
Although Large Language Models (LLMs) from prominent providers like OpenAI, Anthropic, and Google offer extensive capabilities, concerns regarding data privacy and security arise when handling personal data. Utilizing Open Source LLMs presents a viable alternative, ensuring comparable functionality while maintaining control over data usage and mitigating data leakage risks. - Providing Domain-Specific Data Requires Effort
While LLMs offer valuable insights derived from general knowledge derived from public data, their effectiveness in specific domains hinges on fine-tuning with domain-specific datasets. To optimize output quality for particular use cases, organizations must invest in curating and leveraging their unique knowledge bases to complement LLM functionality effectively. You will need to be ready to invest time and resources to prepare and work with your own unique knowledge base to ensure the quality and utility of your generative AI product. - Solving Multi-Tenancy for Data Is Not Trivial
Vector databases are integral to generative AI projects to maximize performance. Vector databases excel at performing tasks unique to generative AI such as: similarity search and retrieval, high-dimensional data handling, and efficient representation of data in compact and efficient ways. However, their incorporation poses challenges, particularly in achieving multi-tenancy. Unlike relational databases common in Software-as-a-Service (SaaS) applications, vector databases necessitate additional steps for seamless integration into auto-scalable systems in order to serve multiple clients or tenants, while keeping their data isolated and secure. - Delivering Scalability in a Cost-efficient Way Is a Big Issue
Current LLM execution processes exhibit sluggish performance, necessitating substantial computational resources for scalability. However, acquiring an abundance of GPUs or resorting to costly solutions from public LLM providers may not be feasible. Addressing scalability requires navigating unique engineering challenges such as semantic caching, security protocols, and token streaming, which off-the-shelf, tutorial-level LLM code often fails to accommodate.
In summary, integrating Generative AI into existing products entails navigating challenges ranging from database architecture to scalability and data specificity. Addressing these challenges demands a strategic approach encompassing technical expertise, data management protocols, and consideration of ethical and privacy implications.
